Transformations#
The aeon.transformations
module contains classes for data
transformations.
Transformations.
All (simple) transformers in aeon can be listed using the aeon.registry .all_estimators utility, using estimator_types=”transformer” tag.
Transformations are categorized as follows:
Category 
Explanation 
Example 

Composition 
Building blocks for pipelines, wrappers, adapters 
Transformer pipeline 
Seriestotabular 
Transforms series to tabular data 
Length and mean 
seriestoseries 
Transforms individual series to series 
Differencing, detrending 
Seriestocollection 
transforms a series into a collection of time series 
Bootstrap, sliding window 
Collection 
Transforms a collection of times series into a new collection of time series 
Padding to equal length 
Hierarchical 
uses hierarchy information nontrivially 
Reconciliation 
Composition#
Pipeline building#

Pipeline of transformers compositor. 

Concatenates results of multiple transformer objects. 

Apply a transformer columnwise to multivariate series. 

Transformer wrapper to delay fit to the transform phase. 

Facilitate an AutoML based selection of the best transformer. 

Wrap an existing transformer to tune whether to include it in a pipeline. 

Invert a seriestoseries transformation. 

Identity transformer, returns data unchanged in transform/inverse_transform. 

Create exogeneous features which are a copy of the endogenous data. 

Constructs a transformer from an arbitrary callable. 
Sklearn and pandas adapters#
A transformer that turns time series collection into tabular data. 

Adapt scikitlearn transformation interface to time series setting. 

Adapt pandas transformations to aeon interface. 
Seriestotabular transformers#
Seriestotabular transformers transform individual time series to a vector of features, usually a vector of floats, but can also be categorical.
When applied to collections or hierarchical data, the transformation result is a table with as many rows as time series in the collection and a column for each feature.
Summarization#
These transformers extract simple summary features.

Calculate summary value of a time series. 

Transformer for extracting time series features. 

Fitted parameter extractor. 
Shapelets, wavelets and convolution#

Random Shapelet Transform. 
Random Dilated Shapelet Transform (RDST) as described in [R1a26faa975731]_[R1a26faa975732]_. 


Scalable and Accurate Subsequence Transform (SAST). 

RandOm Convolutional KErnel Transform (ROCKET). 

MINImally RandOm Convolutional KErnel Transform (MiniRocket). 

MINImally RandOm Convolutional KErnel Transform (MiniRocket) multivariate. 
MINIROCKET (Multivariate, unequal length). 


Multi RandOm Convolutional KErnel Transform (MultiRocket). 

Multi RandOm Convolutional KErnel Transform (MultiRocket). 

Discrete Wavelet Transform Transformer. 
Distancebased features#

Return the matrix profile and index profile for each time series of a dataset. 
Dictionarybased features#
Signaturebased features#

Transformation class from the signature method. 
Feature collections#
These transformers extract larger collections of features.
Transformer for extracting time series features via tsfresh.extract_features. 


Transformer for extracting time series features via tsfresh.extract_features. 

Canonical Timeseries Characteristics (Catch22). 
Seriestoseries transformers#
Seriestoseries transformers transform individual time series into another time series. When applied to collections or hierarchical data, individual series are transformed through broadcasting.
Lagging#

Lagging transformer. 
Elementwise transforms#
These transformations apply a function elementwise.
Depending on the transformer, the transformation parameters can be fitted.

BoxCox power transform. 

Natural logarithm transformation. 

Scaled logit transform or Log transform. 

Cosine transformation. 

Apply elementwise exponentiation transformation to a time series. 

Apply elementsise square root transformation to a time series. 
Detrending#

Remove a trend from a series. 

Remove seasonal components from a time series. 

Remove seasonal components from time series, conditional on seasonality test. 

Remove seasonal components from a timeseries using STL. 

Clear sky transformer for solar data. 
Filtering and denoising#

Filter a times series using the BaxterKing filter. 

Transformer that filters Series data. 

Kalman Filter Transformer. 

Decompose the original data into two or more Thetalines. 
Differencing and slope#

Apply iterative differences to a timeseries. 

Piecewise slope transformation. 
Binning and segmentation#

Bins time series and aggregates by bin. 

Time series interpolator/resampler. 

Interval segmentation transformer. 

Random interval segmenter transformer. 
Missing value imputation#

Missing value imputation. 
Seasonality and DateTime Features#

DateTime feature extraction for use in e.g. tree based models. 

Get Elementwise time elapsed between the timeindex and a reference start time. 

Fourier Features for time series seasonality. 
Autocorrelation series#

Autocorrelation transformer. 

Partial autocorrelation transformer. 
Windowbased series transforms#
These transformers create a series based on a sequence of sliding windows.

Calculate the matrix profile of a time series. 

HOG1D transform. 
Multivariatetounivariate#
These transformers convert multivariate series to univariate.

Concatenate multivariate series to a long univariate series. 
Augmentation#
Augmenter inverting the time series by multiplying it by 1. 


Draw random samples from time series. 
Augmenter reversing the time series. 


Augmenter adding Gaussian (i.e. white) noise to the time series. 
FeatureSelection#
These transformers select features in X based on y.

Select exogenous features. 

Elbow Class Sum (ECS) transformer to select a subset of channels/variables. 

Elbow Class Pairwise (ECP) transformer to select a subset of channels. 
Subsetting time points and variables#
These transformers subset X by time points (pandas index or index level) or variables (pandas columns).

Column selection transformer. 

Index subsetting transformer. 
Panel transformers#
Panel transformers transform a panel of time series into a panel of time series.
A panel transformer is fitted on an entire panel, and not per series.
Equal length transforms#
These transformations ensure all series in a panel have equal length

Pad unequal length time series to equal, fixed length. 

Truncate unequal length time series to a lower bounds. 
Dimension reduction#

Principal Components Analysis applied as transformer. 
SeriestoPanel transformers#
These transformers create a panel from a single series.
Bootstrap transformations#

Creates a population of similar time series. 

Moving Block Bootstrapping method for synthetic time series generation. 
Outlier detection, changepoint detection#

Use HampelFilter to detect outliers based on a sliding window. 

ClaSP (Classification Score Profile) Transformer. 
Hierarchical transformers#
These transformers are specifically for hierarchical data and panel data.
The transformation depends on the specified hierarchy in a nontrivial way.

Prepare hierarchical data, including aggregate levels, from bottom level. 

Hierarchical reconcilation transformer. 